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AI モデル 84760
- GLM-5.3-Flash-GSQ-RCO-3.0bit-Q4Kattn-GGUF↗
neuralll
> **Big thanks to @csantiago78**: the expert cache > here builds on their implementation in llama.cpp PR > #27861 ("GPU-resident LRU cache > for host-offloaded MoE expert weights"), the first to get a working hot-expert > cache into llama.cpp. This fork takes it further: filling all free VRAM, smarter > eviction, CPU/GPU overlap and fused kernels. A variant of pfeifferj/GLM-5.3-Flash-GSQ-RCO-GGUF (3.0-bit) made for faster single-stream decoding of this 117 GB model on consumer GPUs with an expert cache. **Only the non-expert Q8_0 weights changed** (attention, shared experts, dense FFN, output head: Q8_0 to Q4_K, ~8.3 GB of the file). All 126 routed-expert tensors, `token_embd` and every F32/BF16 tensor are copied bit-for-bit from the original. Those Q8_0 weights are streamed by the GPU for every token, so shrinking them speeds up decoding; the experts are unchanged.
- Lumimaid-v0.2-12B-GGUF↗
NeverSleep
NOTE: As explained on Mistral-Nemo-Instruct-2407 repo, it's recommended to use a low temperature, please experiment! As some people have told us our models are sloppy, Ikari decided to say fuck it and literally nuke all chats out with most slop. Our dataset stayed the same since day one, we added data over time, cleaned them, and repeat. After not releasing model for a while because we were never satisfied, we think it's time to come back!
- LFM2-24B-A2B-GGUF↗
NexaAI
- Nous-Hermes-2-Mistral-7B-DPO↗
NousResearch
- GEAR-CHIP↗
NVIDIA
- Llama-2-7B-DMC-4x↗
NVIDIA
- stt_en_fastconformer_hybrid_large_streaming_multi↗
NVIDIA
- gemma-4-26B-A4B-it-Uncensored-MAX-GGUF↗
prithivMLmods
- Qwen3.5-35B-A3B↗
阿里巴巴 · Qwen
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...
- Qwen3-Embedding-8B-GGUF↗
阿里巴巴 · Qwen
The Qwen3 Embedding series model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.